Project Description

Create a K8s CRD for s3gw.
The operator will be written in Go.
The CRD should in the beginning allow an user to create a bucket.

Goal for this Hackweek

The CRD should in the beginning allow an user to create a bucket.

Project

https://github.com/giubacc/s3gw-operator

Looking for hackers with the skills:

golang kubernetes operator s3gw

This project is part of:

Hack Week 22

Activity

almost 2 years ago: gbaccini joined this project.

almost 2 years ago: gbaccini added keyword "golang" to this project.

almost 2 years ago: gbaccini added keyword "kubernetes" to this project.

almost 2 years ago: gbaccini added keyword "operator" to this project.

almost 2 years ago: gbaccini added keyword "s3gw" to this project.

almost 2 years ago: tdehler started this project.

about 2 years ago: gbaccini originated this project.

Comments

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For my personal site I have been using hugo. It works, but I am not satisfied: every time I want to make a change (which is infrequently) I have to read through the documentation again to understand how hugo works. I don't find the documentation easy to use, and the structure of the repository that hugo requires is unintuitive/more complex than what I need. So, I have decided to write my own simple static site generator in Go. It is named Jenny, after my wife.

Goals

Pages can be written in markdown (which is automatically converted to HTML), but other file types are also allowed
Easy to understand and use
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Future maintenance is easy
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https://github.com/adamkpickering/jenny

Mammuthus - The NFS-Ganesha inside Kubernetes controller by vcheng

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As the user-space NFS provider, the NFS-Ganesha is wieldy use with serval projects. e.g. Longhorn/Rook. We want to create the Kubernetes Controller to make configuring NFS-Ganesha easy. This controller will let users configure NFS-Ganesha through different backends like VFS/CephFS.

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A CLI for Harvester by mohamed.belgaied

[comment]: # Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI [comment]: # Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. [comment]: # Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.

Project Description

Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as: harvester vm create my-vm --count 5 to create 5 VMs named my-vm-01 to my-vm-05.

Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.

Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli

Done in previous Hackweeks

Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE

Goal for this Hackweek

The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.

Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it

Issue list is here: https://github.com/belgaied2/harvester-cli/issues

Resources

The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact). Welcome contributions are:

Testing it and creating issues
Documentation
Go code improvement

What you might learn

Harvester CLI might be interesting to you if you want to learn more about:

GitHub Actions
Harvester as a SUSE Product
Go programming language
Kubernetes API

ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

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It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

Overview

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Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
Extensibility: Easily extend functionality with custom plugins and configurations.
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Features

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Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
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Description

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Building and Installing the Plugin

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Build the Plugin: Compile the Go program:

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mv kubectl-clone /usr/local/bin/

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Usage Examples

Clone a Deployment from One Cluster to Another:

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Clone a Service into Another Namespace and Modify Labels:

kubernetes

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Description

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Goals

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add containerized image for ddflare
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add documentation, covering also a sample pod deployment for Kubernetes
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https://github.com/fgiudici/ddflare
https://developers.cloudflare.com/api/
https://book.kubebuilder.io

Mammuthus - The NFS-Ganesha inside Kubernetes controller by vcheng

Description

Goals

Create NFS-Ganesha Package on OBS: nfs-ganesha5, nfs-ganesha6
Create NFS-Ganesha Container Image on OBS: Image
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https://github.com/dwyl/phoenix-liveview-counter-tutorial https://github.com/propedeutica/elixir-k8s-counter

kubectl clone: Seamlessly Clone Kubernetes Resources Across Multiple Rancher Clusters and Projects by dpunia

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Rancher & Kubernetes Docs
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Development Tools
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export RANCHER_URL="https://rancher.example.com"
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go build -o kubectl-clone ./pkg/

Install the Plugin: Move the executable to a directory in your PATH:

mv kubectl-clone /usr/local/bin/

Ensure the file is executable:

chmod +x /usr/local/bin/kubectl-clone

Verify the Plugin Installation: Test the plugin by running:

kubectl clone --help

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Clone a Deployment from One Cluster to Another:

kubectl clone --source-cluster c-abc123 --type deployment --name nginx-deployment --target-cluster c-def456 --new-name nginx-deployment-clone

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Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!

Results: Infrastructure Achievements

We successfully built and automated a containerized stack to support our AI experiments. This included:

a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
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- https://github.com/moio/PyTAG/tree/hackweek24

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Results: Game Design Insights

Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
- more about Bamboo on Dario's site
- more about R3 on Silvio's site (italian, translation coming)
- more about Totoro on Silvio's site

A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

Results: Learning, Collaboration, and Innovation

Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

"Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

The Context: AI + Board Games

operator

ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

Overview

Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
Extensibility: Easily extend functionality with custom plugins and configurations.
Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

Features

distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

Planned features (Wishlist / TODOs)

Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

Install Uyuni on Kubernetes in cloud-native way by cbosdonnat

Description

For now installing Uyuni on Kubernetes requires running mgradm on a cluster node... which is not what users would do in the Kubernetes world. The idea is to implement an installation based only on helm charts and probably an operator.

Goals

Install Uyuni from Rancher UI.

Resources

mgradm code: https://github.com/uyuni-project/uyuni-tools
Uyuni operator: https://github.com/cbosdo/uyuni-operator